PSI - Issue 74

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2025) 000–000 Available online at www.sciencedirect.com ScienceDirect

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Procedia Structural Integrity 74 (2025) 106–113

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Libor Pantělejev Abstract In material science, especially when analyzing SEM and TEM images, the measurement of microstructural features has traditionally relied on manual methods. These methods are slow and prone to human error. A framework employing the BIRCH clustering algorithm and thresholding techniques was developed to automatically segment and categorize microstructural features. The framework can extract quantitative parameters such as cell diameter, aspect ratio, major and minor axis lengths, etc. While cell diameter can be measured using traditional methods like the line-intercept technique, obtaining other parameters manually is significantly more difficult. By utilizing the automated framework, results are more consistent. However, while this framework substantially reduces manual effort and accelerates analysis, challenges remain when addressing highly complex or anomalous images. In such cases, artificial neural networks (ANN) offer a more adaptive and robust solution. Yet, preparing the extensive well-labeled datasets required for ANN is time-consuming and resource-intensive. Semi-automated data preparation strategies are being explored, to minimize these demands by reducing manual input and enhancing the efficiency and scalability of the analysis pipeline. In this work, the cellular structure present in additively manufactured 316L steel was evaluated using the above-mentioned tools to enable potential correlation of the microstructural features with mechanical properties. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Libor Pan tě lejev Keywords: Addtive manufacturing; Machine learning; austenitic steel 316L; Dislocation substructure; Deep learning 1. Introduction Austenitic stainless steel 316L is a widely used engineering material known for its exceptional corrosion resistance, weldability, and biocompatibility (Shih et al., 2004). However, its ability to be strengthened by traditional heat treatment methods is limited due to its stable austenitic structure (Saeidi et al., 2015). Enhancing its strength through Eleventh International Conference on Materials Structure and Micromechanics of Fracture Advanced Semantic Segmentation of Cellular Substructure in Selectively Laser Melted 316L Stainless Steel Tomáš Vražina 1, 2*, Jaromír Brůža 1, 2 , Alice Chlupová 1 , Ivo Šulák 1 , Libor Pantělejev 2 , Tomáš Kruml 1 , Jiří Man 1 1 Institute of Physics of Materials, Czech Academy of Sciences, Žižkova 22, 61600 Brno, Czech Republic 2 Institute of Materials Science and Engineering, Brno University of Technology, Technická 2896/2, 61669 Brno, Czech Republic

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Libor Pant?lejev

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Libor Pantělejev 10.1016/j.prostr.2025.10.041